Postdoc in Applied Statistical Modelling and Machine Learning

Technical University of Denmark
- Section for Statistics and Data Analysis

Location:

Lyngby - Denmark

Salary:

Not Specified

Hours:

Full Time

Contract Type:

Fixed-Term/Contract

Placed On:

16th August 2019

Closes:

15th September 2019

Starting at October 1st 2019, or as soon as possible thereafter, a postdoc

position is offered at the Section for Statistics and Data Analysis, a part of Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU Compute). Our department DTU Compute is an internationally unique academic environment spanning the science disciplines mathematics, statistics and computer science. At the same time we are an engineering department, covering informatics and communication technologies (ICT) in their broadest sense. Finally, we play a major role in addressing the societal challenges of the digital society where ICT is a part of every industry, service, and human endeavor.

You will be working within the area of applied statistics, focusing on synthesizing statistical modeling with tools such as statistical learning; aspects of machine learning, deep learning and standard statistical analysis tools where appropriate, based on a data driven approach. Both method justification in terms of theoretical considerations and practical feasibility will be part of the work.

Responsibilities and tasks

PACE – Proactive Care for the Elderly with Dementia - is a partnership formed by DTU Compute, Center for Design, Innovation and Sustainable Transition at Aalborg University Copenhagen, Skovhuset (Hillerød Municipality), The SIF group, Copenhagen Business Hub and Kullegaard. PACE is financed by Innovation Fund Denmark.

You will, as a part of PACE, explore Big Data to detect changes in practices among elderly with dementia, aiming at preventing hospitalizations, by combining information from already existing technologies. The project is about extracting this information from data, and put it to practical use.

With basis in longitudinal sensor information from intelligent floors, sensor staffs, alarm calls and additional sensor- and other information, you will build a procedure that, for each of the individual nursing home residents followed, will construct a concept of ‘normal behavior’, detected on an automatized basis. You will make the construction in such a way that natural variation in sensor readings etc. is incorporated to not indicate that the resident is changing behavior and will not trigger a violation of the individual ‘normal behavior’ concept. Building this procedure is the main task of the project.

You will detect and classify changes that fall outside normal behavior, such that expected reasons and implications from a specific deviation from normal behavior can be studied, essentially giving a pointer towards which disease (if any) that caused the deviation, and the severity of the implications.

New user interfaces to implement the big data analytics will be designed in participation with you, in a way that matches daily need for healthcare staff, and aim at providing a standard for future nursing homes.

Qualifications

You should have a PhD degree or equivalent academic qualifications within mathematics/statistics, computational science and engineering, engineering, or equivalent areas. Programming skills in at least one language such as R, Matlab, Python, Java or C is essential.

You should, in addition, have an interest in seeing mathematics, statistics and machine learning be put to practical use, and appreciate to operate among professionals from several disciplines far from technical science, while still being placed in a department that has this as its main focus.